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https://github.com/youssef-remah/hypoglycemia_prediction

Intelligent Real-Time Hypoglycemia Prediction System.
https://github.com/youssef-remah/hypoglycemia_prediction

api bluetooth-low-energy deep-learning firebase-firestore flutter mvvm-architecture tensorflow-lite

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Intelligent Real-Time Hypoglycemia Prediction System.

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README 

        
# Hypoglycemia Prediction (GlyCare) App



## Project Overview

This project presents the development of an intelligent real-time hypoglycemia prediction system designed to enhance diabetes management by providing accurate and timely predictions of hypoglycemic events. The system integrates deep learning algorithms within a mobile application that interacts with wearable devices, such as Continuous Glucose Monitoring (CGM) sensors and smartwatches, to collect real-time data on glucose levels, insulin dosages, physical activity, and sleep patterns.



## Project Objectives

- **Develop an AI Model for Hypoglycemia Prediction:** Create an advanced AI model capable of predicting the risk of hypoglycemic episodes for individual patients with T1D.



- **Seamless Integration of Sensor Data:** Integrate data from the "Libre 2" glucose monitoring sensor, smartwatches, and sleep tracking sensors to provide a comprehensive and real-time view of the user's health status.



- **Accurate Predictions of Nocturnal Hypoglycemic Events:** Implement AI and Deep Learning algorithms to enable accurate real-time predictions, specifically focusing on nocturnal hypoglycemic events.



- **Mobile Application Development:** Develop an intuitive mobile application using Flutter, delivering timely alerts, personalized insights, and educational content to users.



- **Proactive Management of Type 1 Diabetes:** Ensure the system empowers individuals with T1D to proactively manage their condition through real-time alerts and user-friendly features.



## Features

- **Real-time Data Collection:** Interacts with CGM sensors and smartwatches to collect real-time data on glucose levels, insulin dosages, physical activity, and sleep patterns.



- **AI-Powered Predictions:** Utilizes Gated Recurrent Units (GRU) networks to predict hypoglycemic events with high accuracy.



- **User-friendly Mobile Application:** Provides timely alerts, personalized insights, and educational content through an intuitive mobile application developed with Flutter.



## Technology Stack

- **Mobile Development:** Flutter



- **Backend:** Firebase Firestore



- **Bluetooth Low Energy (BLE) Technology:** For wireless connections with CGM sensors and fitness trackers



- **AI Model Deployment:** TensorFlow Lite



## Installation and Setup

**1. Clone the Repository:**



`git clone https://github.com/Youssef-Remah/Hypoglycemia_Prediction.git`



**2. Navigate to the Project Directory:**



`cd hypoglycemia-prediction`



**3. Install Dependencies:**



`flutter pub get`



**4. Run the Application:**



`flutter run`



## Usage

- **Connect Devices:** Ensure your CGM sensor and smartwatch are connected and paired with the application.



- **Monitor Data:** View real-time glucose levels, insulin dosages, and other health metrics within the app.



- **Receive Alerts:** Get notified of predicted hypoglycemic events and take preventive measures.



## License

This project is licensed under the MIT License. See the **LICENSE** file for details.



## Acknowledgements

- **Supervised by:** **[Dr. Ahmed Fathy Elnokrashy](https://github.com/nokrashy)**



- **Contributors:** **[Youssef Remah Mohamed](https://github.com/Youssef-Remah)**, **[Mahmoud Elrouby](https://github.com/Mr11011)**, **[Salma Ahmed Ali](https://github.com/SalmaAhmed112)**, **[Sherif Ali Mahmoud](https://github.com/sherif566)**, **[Rawan Saeed Elnagar](https://github.com/RawanElNagar)**